Differential effect of NMDA receptor GluN2C and GluN2D subunit ablation on behavior and channel blocker-induced schizophrenia phenotypes
Bibliographic record
Abstract
block and lack of desensitization. Recent studies have highlighted the unique contribution of GluN2C and GluN2D subunits in various aspects of neuronal and circuit function and behavior, however a direct comparison of the effect of ablation of these subunits in mice on pure background strain has not been conducted. Using knockout-first strains for the GRIN2C and GRIN2D produced on pure C57BL/6N strain, we compared the effect of partial or complete ablation of GluN2C and GluN2D subunit on various behaviors relevant to mental disorders. A large number of behaviors described previously in GluN2C and GluN2D knockout mice were reproduced in these mice, however, some specific differences were also observed possibly representing strain effects. We also examined the response to NMDA receptor channel blockers in these mouse strains and surprisingly found that unlike previous reports GluN2D knockout mice were not resistant to phencyclidine-induced hyperlocomotion. Interestingly, the GluN2C knockout mice showed reduced sensitivity to phencyclidine-induced hyperlocomotion. We also found that NMDA receptor channel blocker produced a deficit in prepulse inhibition which was prevented by a GluN2C/2D potentiator in wildtype and GluN2C heterozygous mice but not in GluN2C knockout mice. Together these results demonstrate a unique role of GluN2C subunit in schizophrenia-like behaviors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".